{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "<  [Shared Axis](07.11-Shared-Axis.ipynb) | [Contents](Index.ipynb) | [8. Input-Output](08.00-Input-Output.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.12 subplot\n",
    "\n",
    "到目前为止，我们已经使用具有相同宽和高的子图。可能会出现这种情况，当我们需要为一些图形的大小。在下面的部分，我们将尝试在这种情况下绘制这样的图。首先，我们将使用`plt.subplot`，来绘制一些特殊的子图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2061a266978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.random.rand(25)\n",
    "y = np.arccos(x)\n",
    "\n",
    "plt.close('all')\n",
    "plt.subplot(221)\n",
    "plt.scatter(x,y)\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.scatter(x,y)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(x,y)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图7.15:绘制不同跨度的子图</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图7.15显示了结果图。`plt.tight_layout()`增加了记号标签的可读性。如果没有使用这个选项，则你可能会得到重叠标签等数字。这个选项防止坐标轴、标题等的重叠。\n",
    "\n",
    "现在，我们使用`subplot2grid`将绘制这类子图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2061a747588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(wspace=0.5,hspace=0.4)\n",
    "\n",
    "ax1 = plt.subplot2grid((3,3),(0,0))\n",
    "ax2 = plt.subplot2grid((3,3),(0,1),colspan=2)\n",
    "ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2,rowspan=2)\n",
    "ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)\n",
    "\n",
    "ax1.scatter(10*x,y)\n",
    "ax2.scatter(10*x,y)\n",
    "ax3.scatter(10*x,y)\n",
    "ax4.scatter(10*x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图7.16:使用subplot2grid绘制不同跨度的子图</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
